Intelligent Control Algorithm Research Articles

Application of Artificial Intelligence and Fuzzy Control Algorithm in Green and Low-Carbon Highway Construction

Smart highways endorse green and low-carbon handling construction infrastructures for promoting pollution-free environments and driving spaces. With the incorporation of artificial intelligence and smart computing features, sophisticated decision systems improve the aim of such smart highway constructions. For promoting green and low-carbon highway construction infrastructure, this research article introduces a pollution control-facilitated recommendation system (PCFRS). The system aims to satisfy eco-friendly driving demands and low-carbon emission requirements. In this system, eco-friendly driving demands and low-carbon emissions are the prime requirements for designing such highways. The research employs fuzzy control algorithms to analyze the relationship between green demands and demand satisfaction factors across different infrastructures based on policies from environmental departments and governing agencies. The green demands as formulated by the environmental department/ governing agencies are used for verifying the demand and relationship factors across various infrastructures. The fuzzy algorithm provides recommendations for optimal highway infrastructure design by identifying the maximum possible combinations of satisfaction factors, enabling cost-effective construction while meeting green environment and low-carbon emission goals. This process is aided by a fuzzy control algorithm with the relationship and demand factor as crisp inputs. The decisions on the maximum possible combinations of the satisfaction factor are used for infrastructure recommendations. The need for optimal design and promoting green environments are optimal across various highway lanes.

Conductive hydrogels‐based self‐sensing soft robot state perception and trajectory tracking

AbstractSoft robots face significant challenges in proprioceptive sensing and precise control due to their highly deformable and compliant nature. This paper addresses these challenges by developing a conductive hydrogel sensor and integrating it into a soft robot for bending angle measurement and motion control. A quantitative mapping between the hydrogel resistance and the robot's bending gesture is formulated. Furthermore, a nonlinear differentiator is proposed to estimate the angular velocity for closed‐loop control, eliminating the reliance on conventional sensors. Meanwhile, a controller is designed to track both structural and nonstructural trajectories. The proposed approach integrates advanced soft sensing materials and intelligent control algorithms, significantly improving the proprioception and motion accuracy of soft robots. This work bridges the gap between novel material design and practical control applications, opening up new possibilities for soft robots to perform delicate tasks in various fields. The experimental results demonstrate the effectiveness of the proposed sensing and control approach in achieving precise and robust motion control of the soft robot.

Microneedles-Based Theranostic Platform: From the Past to the Future.

Fully integrated theranostic devices are highly esteemed in clinical applications, offering immense potential in real-time disease monitoring and personalized care. Microneedles (MNs), as innovative and wearable devices, boast important advantages in biosensing and therapy, thus holding significant promise in the advancement of diagnostic and therapeutic platforms. Encouragingly, advancements in electrochemical sensing technology, micronano fabrication, and biocompatible materials are propelling momentum for MNs-based closed-loop systems, enhancing detection capabilities, biocompatibility, and cost-effectiveness. Moreover, the notable progress in integrating MN chips with other biochips signifies a frontier for growth. Successful clinical trials in target molecule monitoring and drug delivery domains herald excellent clinical translational prospects for the aforementioned theranostic platform. Finally, we delineate both challenges and opportunities in the development of integrated diagnostic and therapeutic MN systems, including continuous monitoring, intelligent control algorithms, safety, and regulatory considerations.

Intelligent control algorithm for industrial robots when performing the assembly operation of cylindrical non-rigid parts

Intelligent control algorithm for industrial robots when performing the assembly operation of cylindrical non-rigid parts

Application of Local Search Particle Swarm Optimization Based on the Beetle Antennae Search Algorithm in Parameter Optimization

Intelligent control algorithms have been extensively utilized for adaptive controller parameter adjustment. While the Particle Swarm Optimization (PSO) algorithm has several issues: slow convergence speed requiring a large number of iterations, a tendency to get trapped in local optima, and difficulty escaping from them. It is also sensitive to the distribution of the solution space, where uneven distribution can lead to inefficient contraction. On the other hand, the Beetle Antennae Search (BAS) algorithm is robust, precise, and has strong global search capabilities. However, its limitation lies in focusing on a single individual. As the number of iterations increases, the step size decays, causing it to get stuck in local extrema and preventing escape. Although setting a fixed or larger initial step size can avoid this, it results in poor stability. The PSO algorithm, which targets a population, can help the BAS algorithm increase diversity and address its deficiencies. Conversely, the characteristics of the BAS algorithm can aid the PSO algorithm in finding the optimal solution early in the optimization process, accelerating convergence. Therefore, considering the combination of BAS and PSO algorithms can leverage their respective advantages and enhance overall algorithm performance. This paper proposes an improved algorithm, W-K-BSO, which integrates the Beetle Antennae Search strategy into the local search phase of PSO. By leveraging chaotic mapping, the algorithm enhances population diversity and accelerates convergence speed. Additionally, the adoption of linearly decreasing inertia weight enhances algorithm performance, while the coordinated control of the contraction factor and inertia weight regulates global and local optimization performance. Furthermore, the influence of beetle antennae position increments on particles is incorporated, along with the establishment of new velocity update rules. Simulation experiments conducted on nine benchmark functions demonstrate that the W-K-BSO algorithm consistently exhibits strong optimization capabilities. It significantly improves the ability to escape local optima, convergence precision, and algorithm stability across various dimensions, with enhancements ranging from 7 to 9 orders of magnitude compared to the BAS algorithm. Application of the W-K-BSO algorithm to PID optimization for the Pointing and Tracking System (PTS) reduced system stabilization time by 28.5%, confirming the algorithm’s superiority and competitiveness.

Crop Monitoring System with Water Moisture Levels Using Controller

This paper presents a Crop Monitoring System (CMS) designed to effectively manage crop water moisture levels through automated irrigation control. The system incorporates soil moisture sensors and environmental sensors for real-time data collection, which is then processed by a controller to determine optimal irrigation schedules and water application rates. By integrating predefined thresholds and algorithms, the controller ensures precise irrigation tailored to specific crop and soil conditions, thus preventing both under- watering and over-watering. Additionally, the CMS features wireless communication capabilities, enabling remote monitoring and adjustment of irrigation settings by farmers. This remote accessibility facilitates timely interventions and optimizations, even in the absence of farmers in the fields. Overall, the CMS enhances crop health, improves resource efficiency, and conserves water in agriculture. By leveraging advanced sensor technologies and intelligent control algorithms, this system offers a sustainable solution for precision agriculture, contributing to increased yields while minimizing water usage and environmental impact. This project intends to make farming as mechanized and labor-free as possible.

Design and implementation of an Autonomous Systems Training Environment framework for control algorithm evaluation in autonomous plant operation

The shortage of trained plant operators who can control complex systems in the process and energy industry is leading to an increasing need for more autonomy of such plants. In future, such systems will be controlled by autonomous agents executing intelligent control algorithms. Due to the critical nature of such plants and processes, new control strategies should not be introduced untested. In order to test suitable algorithms, robust and comprehensive simulation environments are required. In this paper, a framework, called Autonomous Systems Training Environment, is proposed for the evaluation of control algorithms in autonomous plant operations. Furthermore, an exemplary use case for a process engineering system is implemented in Matlab/Simulink, taking into account different levels of control (e.g., regulatory control, operator control, safety control). Faults, which represent a major challenge in the autonomous control, are also considered.

ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).

Case Study of Solar Integration in HVAC Systems: Efficiency and Sustainability Outcomes

This study looks into the environmental advantages, economic feasibility, and technical viability of using solar energy to charge HVAC (Heating, Ventilation, and Air Conditioning) batteries. It also covers issues like the unpredictable nature of solar energy and the requirement for energy-storage technology. This study adds to the ongoing efforts to develop a more resilient and sustainable energy infrastructure by encouraging the incorporation of renewable energy sources into HVAC systems. PV (Photovoltaic) panels collect solar energy, a charge controller manages energy effectively, and a battery storage unit is adapted to meet HVAC needs make up the system. The purpose of solar energy integration into HVAC systems is to decrease dependency on the traditional grid, reduce environmental effects, and improve overall energy efficiency. Intelligent charge control algorithms, real-time monitoring, and predictive maintenance capabilities are some of the solar-powered charging system's key characteristics. These characteristics ensure that the HVAC system runs smoothly and leave as little of an environmental impact as possible while optimizing energy utilization. The system's scalability and adaptability to a wide range of customer needs enable it to serve HVAC applications in both residential and commercial settings.

Study on indoor temperature optimal control of air-conditioning based on Twin Delayed Deep Deterministic policy gradient algorithm

The optimization of HVAC system control affects the thermal comfort of building environment and air-conditioning energy consumption. However, most existing studies concentrate on human thermal comfort and neglects to incorporate air-conditioning energy consumption required to establish a controlled indoor thermal environment within the policy, which results in air-conditioning system high energy consumption. This paper suggests an indoor temperature intelligent control method based on thermal comfort and energy consumption of air-conditioning, and the TD3 algorithm is employed as an intelligent control algorithm to obtain the optimal setpoint that maximizes the joint benefit of the occupant’s thermal comfort and the energy saving rate of air-conditioning system. The air-conditioning indoor temperature intelligent control model based on TD3 algorithm is trained by using the experimental data, and the method is simulated in TRNSYS simulation software to corroborate the efficacy and energy saving rate of the method. The results indicate that the indoor temperature intelligent control model based on thermal comfort and energy consumption of air-conditioning has less optimal adjustment of indoor temperature setpoints, which is more stable for the air-conditioning system and can satisfy the thermal comfort demand at the initial control stage to prevent significant fluctuations in human thermal sensation compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation. Besides, the average daily energy consumption can be reduced by 6.54% and 3.37% respectively, when compared to the linear control method based on thermal sensation and the fuzzy control method based on thermal sensation.

Spacecraft attitude testbed

Spacecraft attitude determination and control systems (ADCS) stand as a pivotal factor for ensuring the success of missions. As satellite sizes continue to decrease, the need for more intelligent and efficient control algorithms becomes increasingly pronounced. In response to this imperative, we propose the development of a scaled-down testbed specifically designed for evaluating satellites (ACDS). This testbed will demonstrate effective control of a CubeSat through the use of Helmholtz coils, magnetic torquer solenoids, reaction wheels and inexpensive student-made components and available components off the shelf (COTS) in the San Diego State University SPACE Lab. Referred to as the Spacecraft Attitude Testbed (SAT), this custom-designed 3-DoF experimental attitude platform replicates the conditions experienced by a weightless satellite in space. The objective of this research paper is to improve the understanding of how to manufacture a testbed and components like those within this paper, expose some of the issues encountered and improve the outcomes for future academic testbeds. Within this study, the testbed employs torque application to magnetic torquers and reaction wheels to regulate attitude. In summary, this research endeavors to establish a comprehensive and cost-effective testbed platform dedicated to the assessment of spacecraft attitude control and determination systems. It not only seeks to enhance our understanding of advanced control algorithms but also aims to contribute valuable insights into fault detection and adaptive control strategies, ultimately bolstering the efficiency and robustness of satellite operations.

Experimental investigation and intelligent control of the magnus anti-rolling device for ship stability at zero speed

Under zero-speed conditions, ships are particularly susceptible to the effects of waves, which directly impact the safety of the vessel. A ship anti-rolling device based on the Magnus effect is designed to mitigate rolling motions across a full range of speeds, thereby enhancing the vessel's stability. This study presents an experimental investigation and intelligent control of Magnus anti-rolling devices aimed at enhancing ship stability at zero speed. The test setup, intelligent control algorithm, and experimental procedures specifically tailored for evaluating the Magnus anti-rolling device were designed. Following this, a comprehensive analysis was conducted to assess the effects of different cylinder geometries, swinging speeds, initial roll angles, and control methods on the anti-rolling characteristics of the device. Results demonstrate that the intelligent control method achieves an average anti-rolling efficiency of 89%. Additionally, the optimised geometric model of the Magnus anti-rolling device exhibits improved anti-rolling efficiency relative to the original model. The study confirms the stability and robustness of the intelligent Magnus anti-rolling device and suggests future research directions for practical applications aboard full-scale vessels in complex marine environments.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Modular Intelligent Control System in the Pre-Assembly Stage

This paper presents a novel approach to developing fully automated intelligent control systems for use within production-based organizations, with a specific focus on advancing research into intelligent production systems. This analysis underscores a prevailing deficiency in control operations preceding assembly, where single-purpose control machines are commonly utilized, thus presenting inherent limitations. Conversely, while accurate multipurpose measurement centers exist, they often fail to deliver comprehensive quality control for manufactured parts due to cost and time constraints associated with the measuring process. The primary aim in this study was to develop an intelligent modular control system capable of overseeing the production of diverse components effectively. The modular intelligent control system is designed to meticulously monitor the quality of each module during the pre-assembly phase. By integrating sophisticated sensors, diagnostic tools, and intelligent control mechanisms, this system ensures precise control over module production processes. It facilitates the monitoring of multiple parameters and critical quality features, while integrated sensors and diagnostic methods promptly identify discrepancies and inaccuracies, enabling the swift diagnosis of issues within specific modules. The system’s intelligent control algorithms optimize production processes and ensure synchronization among individual modules, thereby ensuring consistent quality and performance. Notably, the implementation of this solution reduces inspection time by an average of 40 to 60% compared to manual inspection methods. Moreover, the system enables the comprehensive archiving of measurement data, eliminating the substantial error rates introduced by human involvement in the inspection process. Furthermore, the system enhances overall project efficiency, predictability, and safety, while allowing for rapid adjustments in order to meet standards and requirements. This innovative approach represents a significant advancement in intelligent control systems for use in production organizations, offering substantial benefits in terms of efficiency, accuracy, and adaptability.

Advanced intelligent optimization for electric vehicle charging stations integrated with photovoltaic system and battery storage in commercial buildings

This paper presents a sophisticated four-stage optimization and intelligent control algorithm tailored for two-way electric vehicle charging (EVC) stations integrated with advanced photovoltaic systems and fixed battery energy storage in commercial buildings. The primary objective is to minimize operating costs while prioritizing customer satisfaction within a dynamic and uncertain energy landscape. Our algorithm optimizes the scheduled charging and discharging of electric vehicles (EVs), local battery storage (BS) units, grid power supply, and deferred loads to balance instantaneous supply and demand. The first stage focuses on developing optimal energy management plans for the day ahead, considering factors such as projected energy production, anticipated EVC demand, and building energy consumption patterns. Building on this foundation, the second stage introduces multilayer EV charging price structures and optimizes participation rewards for discharging, dynamically addressing EV charging patterns and price sensitivities. Approaching the commissioning timeline, the third stage refines energy management plans for the upcoming hours using real-time data and forecasts, adapting to evolving conditions for optimal resource allocation. The final stage involves real-time control and the implementation of optimized programs, dynamically adjusting charge/discharge processes, grid interactions, and load deferral to maintain supply-demand balance and minimize operating costs. Our algorithm enhances system resilience in unpredictable conditions, providing compelling incentives for active EV user participation. Coordinating the integrated system efficiently, including the commercial building’s energy load, ensures reliable service to customers while reducing costs. Extensive case studies and a comparative analysis validate the algorithm’s efficiency in significantly reducing operating costs and enhancing resilience to uncertainty. The paper concludes by highlighting the algorithm’s pioneering role in intelligent EV charging station (CHS) management, offering a cost-effective, customer-oriented, and dynamic energy control strategy for advancing global energy practices.

ASCD: Automatic sensing and control device for crop irrigation scheduling

Traditional agricultural irrigation systems suffer from wasting a lot of water allocated for irrigation through evaporation and drainage. To address water waste and increase crop productivity, irrigation scheduling is used, as irrigation scheduling depends on four elements: soil moisture, soil characteristics, weather conditions, and plant type. This paper proposes a new design for monitoring agricultural conditions and controlling the amount of soil moisture by determining the required irrigation water ratios and the ideal time period for irrigating crops. An intelligent control and monitoring algorithm was created using experimental data for a wide range of plants (for soil moisture from 21 to 80 kPa), where plants were classified into three groups according to their common irrigation starting point. Based on the proposed plant classification, three models were provided, where irrigation ratios as the required parameter, soil moisture, plant type, and time interval as input parameters. This algorithm was used to program a custom-made automatic sensor and control device (ASCD). The ASCD is equipped with two input ports to communicate with two types of soil moisture sensors (a resistive sensor and a capacitive sensor) and with three output ports to drive three types of electronic irrigation valves. The ASCD can read from one sensor and drive one electronic valve at a time, the choice of which is made by the farmer. ASCD based the new algorithm showed high accuracy compared to a number of experimental results. The average absolute relative deviation (AAPD) of the new models and experimental data is 5.46 %. The application of the new algorithm shows a reduction in the amount of water used for crop irrigation during the day versus irrigation at night. On the other hand, ASCD has proven its success in sensing and controlling, and it works automatically and independently.

Heat exchanger control: Using deep-Q network

To achieve stable operation of district heating systems and improve the performance of heat exchange station control systems, this paper explores the application of intelligent control algorithms for controlling district heating systems. Initially, a heat exchanger neural network model is established. Secondly, a mass regulation controller based on a deep reinforcement learning algorithm is designed to accurately track the desired outlet temperature of the secondary side set point. Finally, the performance of the controller in terms of tracking response and disturbance suppression is verified using an example of a district heating system. The experimental results demonstrate that the controller exhibits minimal overshoot, with the outlet temperature of the secondary side deviating by less than 1 % when the controller setting value changes. Additionally, during disturbance suppression response, the deviation of the outlet temperature of the secondary side remains within ±2 °C. Compared to other controllers, the designed controller exhibits superior dynamic performance across various operating points and under different disturbances. Additionally, it outperforms traditional control methods in terms of stability and disturbance rejection capabilities.

Three-layer data center-based intelligent slice admission control algorithm for C-RAN using approximate reinforcement learning

Three-layer data center-based intelligent slice admission control algorithm for C-RAN using approximate reinforcement learning

Dynamic routing optimization in software-defined networking based on a metaheuristic algorithm

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

Design and development of intelligent control strategies and algorithms for automated control of biotechnical objects under uncertainty

Uncertainty of environmental conditions is typical for most biological and agricultural systems. This uncertainty has recently become a serious challenge because of global climate changes. Due to delayed response to unpredictable environmental changes, the functioning of these systems is not always optimal. Another source of uncertainty is the adaptive response of a biological system, which usually does not coincide with the chosen objective function. This study focuses on developing intelligent control strategies to improve the performance of biotechnical systems under uncertainty. We use cause–effect diagrams to analyze the effect of uncertainties on the key performance indicators (KPIs). Our concept of intelligent control is based on the statistical evaluation of environmental uncertainties, while the decision-making is based on game theory. This intelligent control concept is universal and could be applied to different classes of biotechnical systems, from small animal farms to large industrial plants. Sugar production is considered as one of the objects of research, which requires minimizing sugar losses in four main unit operations: diffusion, purification, evaporation and crystallization. Each unit operation was modeled with the artificial neural networks (ANN) trained on the laboratory datasets. Based on simulated or real inputs from the technological process, ANNs predict outputs in terms of quality, losses, and energy efficiency. In stationary mode, ANNs are used to predict optimal technological regimes. They are used to correct technological regimes to maximize the objective function in real time. The application of intelligent algorithms improved process performance and energy efficiency.